Data Engineering
From zero to production-grade DE — 46 modules, no prerequisites
46 Modules. Zero to Advanced.
Follow in order. Each module builds on the last. Every concept is introduced exactly when you need it, not before.
What is Data? How Computers Store Information
Before you engineer data you need to understand what data actually is — bits, bytes, files, and memory. Built from scratch so nothing feels like magic.
What is Data Engineering?
The role, the career, a real day-in-the-life at a Bangalore startup, and why this job exists at all. The clearest explanation you will find anywhere.
How Data Moves Through a Company
The complete end-to-end story — from the moment data is created at a source, to the dashboard a business leader looks at every morning.
The Data Engineering Ecosystem — Map of All the Tools
There are hundreds of tools in this space. This module maps all of them, explains why so many exist, and shows exactly where each one fits.
Data Engineer vs Analyst vs Scientist vs ML Engineer
Clear, permanent boundaries between the four most confused roles in all of tech. Know exactly where you fit and where each role ends.
Data Engineering in the Indian Job Market (2026)
Real salary data by city and company type, top hiring companies, skills in demand, and how to break into DE from a non-IT background.
Structured, Semi-Structured and Unstructured Data
The three categories every data engineer works with daily — what makes each one different and what each demands from your pipeline.
Data Formats — CSV, JSON, Parquet, Avro, ORC
Not just what each format is — but when to use it, what it costs in storage and compute, and what breaks when you choose the wrong one.
Databases — What They Are and How They Work Internally
Storage engines, B-trees, indexes, buffer pools, WAL — the inside story that makes you 10× better at every database you ever use.
SQL vs NoSQL — The Real Difference
Why the choice matters, what each one trades off, and how to pick the right store for any situation — without cargo-culting trends.
Data Warehouse vs Data Lake vs Lakehouse
Three different answers to the same question: where do we keep all this data? The honest trade-offs, explained simply.
Schemas, Tables, Keys and Indexes — The Building Blocks
The building blocks of every database. Understanding these deeply separates good engineers from great ones.
ACID Properties and Transactions
Why ACID exists, what each property means in practice, and what actually happens when a transaction fails halfway through.
Python for Data Engineering
Not Python 101. Python for pipelines — file I/O at scale, REST APIs, error handling, exponential backoff, logging, generators, and testable code.
SQL for Data Engineers — Beyond the Basics
Window functions, complex CTEs, deduplication patterns, SCD in SQL, and the advanced queries every DE interview actually tests.
Linux and Shell Scripting for Data Engineers
Navigate, process files, write bash scripts, schedule cron jobs, and monitor processes — everything you need from the terminal.
Git and Version Control for Data Projects
Branching strategies, managing large files, pre-commit hooks, and semantic versioning — for data teams specifically.
Working with APIs — REST, Auth, Pagination, Rate Limits
Every data engineer pulls from APIs. Build robust ingestion classes with retries, pagination, OAuth, and checkpointing.
Working with Files at Scale
Partitioning strategies, compression algorithms, the small file problem, and how columnar storage works internally.
What is a Data Pipeline? Anatomy and Design Principles
The most important concept in data engineering. Every component, how they connect, and the principles that make a pipeline good.
Batch vs Streaming vs Micro-Batch
Three processing models with real trade-offs. Know each deeply enough to pick the right one for any business problem.
ETL vs ELT — History, Difference, When to Use Each
Why ETL dominated for 30 years, why ELT replaced it, and the situations where the old way is still the right way.
Data Ingestion Patterns — Full Load, Incremental, CDC
The three ways to pull data from a source system. Most engineers only know one. Learn all three and when each one breaks.
Change Data Capture (CDC) — How It Works Under the Hood
Log-based, trigger-based, query-based CDC — the internals, the trade-offs, and the production gotchas nobody writes about.
Building a Batch Pipeline From Scratch
A complete Python pipeline: extract → validate → transform → load → checkpoint. Full code, full errors, full production decisions explained.
Idempotency, Atomicity and Pipeline Restartability
Why every pipeline must be safe to re-run. The two properties that separate toy pipelines from production ones.
Error Handling, Retries and Dead Letter Queues
What happens when a pipeline fails at 3am. How to build systems that survive the real world without waking anyone up.
Pipeline Orchestration — What a Scheduler Does
The concepts behind orchestrators — DAGs, dependencies, triggers, backfill — without tying you to any single tool.
Data Lake Architecture — Design, Zones and Anti-Patterns
How to design a data lake that stays useful for years — and the patterns that turn it into an unmaintainable swamp.
Medallion Architecture — Bronze, Silver, Gold
The most popular data lake design pattern at modern companies. What each layer does, why it exists, and how to implement it.
Data Warehouse Concepts — Columnar Storage and Distribution
How a warehouse actually stores and queries data at scale. The internals that explain both the performance and the cost.
Lakehouse Architecture — Why It Exists and How It Works
The best of warehouse and lake in one architecture. Why the industry moved here and what problems it actually solves.
Data Modelling — Dimensional, Star and Snowflake Schema
How to organise data so analysts can query it fast and intuitively. The art behind every well-designed analytics table.
Slowly Changing Dimensions — SCD Types 1, 2 and 3
One of the most-tested DE interview topics. What happens when a dimension — like a customer address or job title — changes over time.
Data Vault 2.0 — Hubs, Links and Satellites
The advanced modelling pattern used by large enterprises. Flexible, auditable, and built to survive the real world changing.
Data Quality — Dimensions, Testing and Validation
How to know your data is trustworthy. The six quality dimensions, how to test for each, and what breaks when you skip this.
Data Observability — Metrics, Logging and Anomaly Detection
When pipelines run in production, how do you know something is wrong before your users do? Observability answers that.
Data Governance — Catalogues, Lineage and Access Control
Who owns the data, who can access it, where did it come from, where is it used. Four questions governance must answer.
Security and Compliance for Data Engineers
GDPR and the India DPDP Act — what they mean for your pipelines and how to build systems that are compliant by design.
Streaming Data — What It Is and How It Works
Event-driven architecture, producers, consumers, offsets, consumer groups — the concepts without a tool tutorial.
Message Brokers and Queues — Internal Mechanics
How messages flow from producer to consumer. Durability, ordering, replayability — the inside story without the tool noise.
Distributed Systems for Data Engineers
CAP theorem, partitioning, replication, fault tolerance — explained for data engineers, not software architects.
Performance Tuning and Cost Optimisation
I/O bound vs CPU bound vs network bound. How to profile any pipeline, find the bottleneck, and fix it without rebuilding everything.
DataOps and CI/CD for Data Pipelines
How to ship pipeline changes like a professional — testing, staging, rollback, and automated deployments.
Infrastructure as Code for Data Engineers
Provision cloud data infrastructure with Terraform — storage accounts, pipelines, clusters, and secrets — so your environments are reproducible, version-controlled, and never "it works on my machine".
Data Engineering System Design
How to design any data system from scratch. Framework, trade-offs, capacity estimation — for both interviews and real work.
Interview Prep — 60 Complete Answers
60 complete answers across Python, SQL, pipelines, modelling, architecture, and behavioural questions — written at senior engineer depth.
Modules are dropping weekly.
Start with Module 01 the moment it goes live. Each module is self-contained enough to read on its own — but follow the order. Every concept earns the next one.
Discussion
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